AI-Enabled Employee Behavior Analysis System (Computer Vision Based) Overview The proposed system is an AI-powered employee behavior analysis platform that utilizes camera feeds and deep learning models built with PyTorch to analyze employee activities within the workplace. The system automatically detects, classifies, and records employee behavior patterns to support productivity insights, operational efficiency, and compliance reporting. Objectives • Monitor employee presence and activity using AI-enabled cameras • Analyze time spent on work-related and non-work-related activities • Provide visual evidence (captured images) with AI-generated comments • Generate accurate time-based reports for management review Key Features 1. Computer Vision & AI Analysis • Real-time video processing using PyTorch-based deep learning models • Human detection and tracking using models such as: • YOLO / Faster R-CNN (object detection) • DeepSORT (person tracking) • Action recognition models (CNN + LSTM / Transformers) • Face and posture recognition (optional and configurable) 2. Behavior Classification The system classifies employee behavior into predefined categories, such as: • Working at desk • Coffee break • Out of office • Mobile phone usage • Idle or inactive time Each detected activity is timestamped and continuously analyzed to calculate total duration. 3. Time Tracking & Analytics • Automatic calculation of: • Total working minutes • Break duration (coffee, idle time) • Time spent outside the office • Excessive mobile phone usage • Daily, weekly, and monthly summaries • Threshold-based alerts for abnormal behavior patterns 4. Image Capture with AI Commentary • System captures images when a behavior change is detected • AI-generated contextual comments are attached, such as: • “Employee away from workstation for more than 15 minutes” • “Prolonged mobile phone usage detected during working hours” • Images and comments are stored securely for audit and review 5. Data Storage & Reporting • Centralized database for: • Activity logs • Time metrics • Image evidence • Dashboard with visual analytics (charts, timelines, heatmaps) • Exportable reports (PDF, Excel) for HR and management Technology Stack • AI Framework: PyTorch • Computer Vision: OpenCV, TorchVision • Models: YOLO, ResNet, LSTM/Transformer-based action recognition • Backend: Python (FastAPI / Flask) • Database: PostgreSQL / MongoDB • Frontend Dashboard: React / Power BI / Grafana • Deployment: On-premise or secure private cloud Security & Compliance • Role-based access control (RBAC) • Encrypted data storage and transmission • Configurable privacy rules (face blurring, restricted zones) • Compliance with local labor and data protection regulations Business Value • Improves productivity visibility without manual supervision • Provides data-driven insights for workforce optimization • Reduces dependency on manual attendance and observation • Enhances operational transparency and accountability